J Neurophysiol 96: 1646 –1657, 2006;
doi:10.1152/jn.00009.2006.
Innovative Methodology
Decomposition of Surface EMG Signals
Carlo J. De Luca,1,3 Alexander Adam,1 Robert Wotiz,2 L. Donald Gilmore,1 and S. Hamid Nawab2,3
1
NeuroMuscular Research Center, 2Department of Electrical and Computer Engineering, and 3Department of Biomedical Engineering,
Boston University, Boston, Massachusetts
Submitted 4 January 2006; accepted in final form 25 May 2006
De Luca, Carlo J., Alexander Adam, Robert Wotiz, L. Donald
Gilmore, and S. Hamid Nawab. Decomposition of surface EMG signals.
J Neurophysiol 96: 1646 –1657, 2006; doi:10.1152/jn.00009.2006.
This report describes an early version of a technique for decomposing
surface electromyographic (sEMG) signals into the constituent motor
unit (MU) action potential trains. A surface sensor array is used to
collect four channels of differentially amplified EMG signals. The
decomposition is achieved by a set of algorithms that uses a specially
developed knowledge-based Artificial Intelligence framework. In the
automatic mode the accuracy ranges from 75 to 91%. An Interactive
Editor is used to increase the accuracy to ⬎97% in signal epochs of
about 30-s duration. The accuracy was verified by comparing the
firings of action potentials from the EMG signals detected simultaneously by the surface sensor array and by a needle sensor. We have
decomposed up to six MU action potential trains from the sEMG
signal detected from the orbicularis oculi, platysma, and tibialis
anterior muscles. However, the yield is generally low, with typically
ⱕ5 MUs per contraction. Both the accuracy and the yield should
increase as the algorithms are developed further. With this technique
it is possible to investigate the behavior of MUs in muscles that are
not easily studied by needle sensors. We found that the inverse
relationship between the recruitment threshold and the firing rate
previously reported for muscles innervated by spinal nerves is also
present in the orbicularis oculi and the platysma, which are innervated
by cranial nerves. However, these two muscles were found to have
greater and more widespread values of firing rates than those of large
limb muscles.
INTRODUCTION
The electromyographic (EMG) signal is composed of the
action potentials from groups of muscle fibers organized into
functional units called motor units (MUs). This signal can be
detected with sensors placed on the surface of the skin or with
needle or wire sensors introduced into the muscle tissue. When
only two or three MUs in the vicinity of the sensors are active,
it is usually possible to visually identify most of the individual
MU action potentials because the incidence of superposition
among the individual MU action potentials is relatively low.
However, when the EMG signal contains the activity of four or
more MUs the individual action potentials become, in large
part, indistinguishable to the naked eye because the incidence
of superposition among two or more MU action potentials
becomes numerous and the shapes of the MU action potentials
may approach in similarity.
In many cases it is desirable to study and/or use the information contained in the timing of the discharges of individual
motor units, such as in investigations for furthering the understanding of how motor units are controlled by the CNS in
Address for reprint requests and other correspondence: C. J. De Luca,
NeuroMuscular Research Center, 19 Deerfield Street, Boston, MA 02215
(E-mail: cjd@bu.edu).
1646
generating force or for assessing the degree of dysfunction in
upper motoneuron diseases such as cerebral palsy, Parkinson’s
disease, amyotrophic lateral sclerosis (ALS), stroke, and other
disorders. This may be achieved by “decomposing” the EMG
signal. The concept is depicted in Fig. 1. A decomposed EMG
signal provides all the information available in the EMG
signal. The timing information provides a complete description
of the interpulse interval, firing rate, and synchronization
characteristics. The morphology of the shapes of the MU
action potentials provides information concerning the anatomy
and health of the muscle fibers.
From a practical perspective, it is desirable to obtain such
information from the signal detected from a single sensor that
is as unobtrusive as possible and that detects MU-rich EMG
signals rather than a plurality of sensors that detect MU-poor
EMG signals.
There have been numerous and varied approaches to extracting action potentials from neural and muscle activity over the
past three decades. The mid-to-late 1960s produced a flurry of
computer-based activity directed at identifying the individual
action potentials and discharge times of neural activity by
shape discrimination. Dominant among these pioneering attempts were the works of Gerstein and Clark (1964), Simon
(1965), Keehn (1966), and Glaser and Marks (1966). Applications to separate the EMG signals did not appear until a full
decade later (Andreassen 1983; De Figueiredo and Gerber
1983; De Luca and Forrest 1972; De Luca et al. 1982a,b;
Guiheneuc et al. 1989; LeFever and De Luca 1978; Mambrito
and De Luca 1984; McGill et al. 1985). This initial flurry was
followed by sustained interest during the past two decades
(Broman 1988; Christodoulou and Pattichis 1999; Fang et al.,
1999; Hochstein et al. 2002; Iani et al. 1994; Jongen et al.
1996; Loudon et al. 1992; Nawab et al. 2004a,b; Stashuk and
de Bruin 1988; Türker et al. 1989; Zennaro et al. 2001; among
many others). To date all the above approaches and techniques
that have been able to identify individual MU action potentials
in the superimposed EMG signal and provide useful physiological information have used indwelling sensors to detect the
signal. These types of sensors have obvious disadvantages
arising from their invasive nature.
In this report we describe a successful attempt at accurately
decomposing EMG signals detected from surface sensors.
BACKGROUND
For the past three decades we have been improving a
technique that we have termed Precision Decomposition I (PD
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DECOMPOSITION OF SURFACE EMG SIGNALS
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FIG. 1. Pictorial outline of the decomposition of the surface EMG signal into its
constituent motor unit action potentials.
(Adapted from De Luca et al. 1982a.)
I). The evolution of this technique, specifically designed to
enable physiological experiments, has been described by LeFever and De Luca (1978), LeFever and De Luca (1982), Mambrito and De Luca (1984), Broman (1988), and De Luca and
Adam (1999). The technique has been useful for decomposing
indwelling EMG (iEMG) signals detected by needle sensors
during isometric contractions and has been used in numerous
physiological studies (see, e.g., Adam and De Luca 2003,
2005; Adam et al. 1998; De Luca et al. 1982a,b; Erim et al.
1999; Finsen et al. 2006; Kamen and De Luca 1989; Makasado
et al. 1995; Masuda and De Luca 1991; Roark et al. 2002;
Sogaard 1995; Westad and Westgaard 2005). Briefly, the
technique consists of identifying action potentials in the iEMG
signal and assigning them to specific motor units by classifying
the shapes and amplitudes of the action potentials. The assignments of the action potentials are made on the basis of template
matching and the probability of firing of the individual motor
units being tracked. Slight modifications in the shape of the
action potential can be tracked by adapting the templates to
these modifications. Superpositions of action potentials are
resolved. Artificial Intelligence techniques are used to accomplish all these tasks. These algorithms were able to decompose
no more than eight MU action potential trains in the iEMG
signal with an accuracy of 60 to 70% in an automatic mode,
requiring 12-min processing time for 1-s acquisition time (De
Luca et al. 1982a,b). The technique has provisions for using an
Interactive Editor program to obtain decomposition accuracies
of 100% in some records of EMG signals.
Throughout this communication, the decomposition accuracy for the ith decomposable motor unit train is defined as
A共i兲 ⫽
NFIR共i兲 ⫺ NFN共i兲 ⫺ NFP共i兲
⫻ 100%
NFIR共i兲
where NFIR(i) is the number of true firings of the MU and
NFN(i) and NFP(i) are respectively the number of false negatives and the number of false positives produced by the
decomposition algorithm for that MU. The term “true firings”
refers to the firings that were obtained by an expert operator
using the Interactive Editor on automatically decomposed data.
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The overall decomposition accuracy for a signal with N decomposable MU trains is then obtained as
A⫽
1
N
冘
N
A共i兲
i⫽1
The rationale behind this unweighted average is that the accurate decomposition of any MU train is of the same significance
as that of any other MU train regardless of its duration, number
of firings, and so forth.
Recently, we made significant improvements to the algorithms, rendering a considerably more powerful version that
we have labeled Precision Decomposition II (PD II). The new
algorithms are based on our own Artificial Intelligence knowledge-based approach specifically designed to manage signalprocessing algorithms that perform two main categories of
functions: 1) they identify differences in shapes and track
changes in the shapes of the action potentials under a variety of
conditions and 2) they resolve complex superpositions. The
algorithms have been described in publications by Nawab and
Lesser (1992), Winograd and Nawab (1995), Hochstein et al.
(2002), and Nawab et al. (2004a,b). We succeeded in automatically decomposing iEMG signals from the quadrifilar needle
sensor (De Luca et al. 1982a) and later with quadrifilar wire
sensors (De Luca and Adam 1999) with typically 10 MU action
potential trains with an accuracy of typically 85% with a
processing time eightfold that of the acquisition time. In one
extraordinary case we were able to decompose automatically
an iEMG signal containing 14 MU action potential trains, of
which the most significant eight could be detected with an
accuracy of 98% (Nawab et al. 2004a).
The quadrifilar needle sensor has the advantage that it can be
repositioned after an insertion or, if necessary, relocated,
thereby increasing the probability of obtaining a quality signal
that can be decomposed. The fine-wire version may be placed
in deep muscles located under an overlying layer of muscle and
it usually provides no discomfort once inserted; however, it has
some disadvantages. Once inserted it cannot be precisely
relocated within the muscle. One can pull the wire out fractions
of a millimeter, although this procedure can be done only once
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DE LUCA ET AL.
or twice and with little control over the precise placement of
the sensor.
Both versions of sensors have inherent limitations:
●
They must be inserted into the muscle. This requires a
clinical preparation involving sterilization of the sensors
and the environment where the insertion is to be made.
● They render difficult or, in some cases, preclude the
investigation of MU firing properties in muscles located in
sensitive and dangerous areas, such as the muscles of the
lips, eyelids, tongue, and most facial muscles, as well as
other regions.
● They carry the risk—albeit low— of infection and disease
transmission.
● They cause minor damage to the muscle tissue from which
they are detecting the signal, which in turn influences the
shapes of the action potentials.
● They are not well tolerated by individuals who have
needle aversion, such as children.
● Once these sensors are inserted, the subject must remain
very steady. A minor movement of 0.1 mm may cause the
shapes of the MU action potentials to change, thus precluding the continued identification of a specific unit and
generally incapacitating the decomposition algorithms
from identifying actions potentials in the remainder of the
contraction.
Many of the disadvantages and limitations of indwelling
sensors can be mitigated by developing technology that uses a
sensor to detect the surface EMG (sEMG) signal and algorithms that can decompose the sEMG signal. Such an approach
presents difficult challenges because the sEMG signal is more
complex than that detected by the relatively more selective
indwelling sensors.
The notion of decomposing the sEMG signal has been
considered by many for two decades (e.g., Masuda et al. 1985)
for the obvious reason that it can be used by nonclinical
researchers interested in motor control. Kleine et al. (2000)
were able to decompose up to five concurrently active MUs
obtained in a multistep, highly user interactive template-matching procedure from surface EMG signals detected with a
128-channel sensor during a low-level (5%) maximal voluntary
contraction (MVC). In the past two years, there has been
increased interest in the problem of decomposing the sEMG
signal. Östlund et al. (2004) and Holobar and Zazula (2004)
investigated the problem with various signal-processing techniques, but the few results presented were restricted to contraction levels of ⱕ10% MVC, included no direct measure of
accuracy, and assumed stationary MU action potential shapes
and uncorrelated discharge behavior. Gazzoni et al. (2004)
described a technique that automatically decomposes ⱕ10
simulated MU trains and detects, in select cases, two to three
MU trains from real sEMG signals, but makes no attempt at
decomposing superimposed action potentials. The PD I technique that we used two decades ago (LeFever and De Luca
1978) was similarly tested, with perfect results, on simulated
EMG signals, but failed miserably when applied to real EMG
signals. We argued the essential importance of testing decomposition techniques on real EMG signals in Mambrito and De
Luca (1984). Most recently, Garcia et al. (2005) presented a
semiautomatic decomposition algorithm to extract the average
firing rates of one to two MUs from EMG signals detected with
a surface electrode array. No accuracy measures for these
results were given, but the authors provide a success rate of
19/26 complete firing trains for a set of synthetic EMG signals
composed of ⱕ10 MU action potential trains.
In this report we describe a technology that is capable of
decomposing several simultaneous MU action potential trains
from real sEMG signals. Although it is in the early stages of
development, we can demonstrate its efficacy. In some cases
the present version of this new technology yields an accuracy
similar to that obtained by our early algorithms of PD I using
indwelling sensors. We refer to our new technique for the
decomposition of sEMG signals as Precision Decomposition
III (PD III). When fully developed, PD III should find applications in neurology, ergonomics, sports medicine, physical
medicine, and space medicine.
METHODS
The surface sensor array
We chose a sensor design that was similar to that of the original
needle sensor, but with larger dimensions. As shown in Fig. 2A, the
sensor consists of four cylindrical probes (0.5 mm diameter) with
blunted ends that protrude from the housing so that when pressed
against the skin they make a surface indentation. The probes are
placed at the corners of a 3.6 ⫻ 3.6-mm square. The diameter of the
probes was chosen to be as small as possible without piercing the skin
when pressed forcefully. As in the intramuscular sensors mentioned
above, each probe provides a detection surface. The leads are connected to the inputs of differential amplifiers. The four detection
surfaces provide six differential combinations or channels. Signals
from four of these channels are amplified and filtered with a bandwidth of 250 Hz to 2.0 kHz to remove any movement artifact at the
low end of the spectrum and any excessively long tails of the action
potentials. The signals are then stored. This arrangement is shown in
Fig. 2B. We found that for the sEMG signals the use of four
FIG. 2. A: surface electromyographic
(sEMG) sensor array containing the 4 pins
that detect the sEMG signals. B: differential
combinations that produce 4 channels of
sEMG signals. Detected signals from each
channel are band-pass filtered before being
decomposed by the algorithms.
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DECOMPOSITION OF SURFACE EMG SIGNALS
differential channels improved the accuracy of the decomposition in
PD III, whereas for iEMG signal decomposition in PD II, three
differential channels were sufficient.
We arrived at the configuration and dimensions of the surface
sensor array heuristically and are currently investigating other configurations. Ideally, the preferred array should maximize the difference in the detected shapes of the motor units while retaining physical
dimensions that are sufficiently small so that it can be used on small
muscles, such as those in the face, and is easy to manage when pressed
against the skin.
Acquisition of sEMG signal
Three volunteers with no known neurological disorders participated
in the tests. All subjects read and signed an informed consent form
approved by the Institutional Review Board of Boston University. To
detect the signal, the surface sensor array was pressed against the skin
above the muscle of interest. No skin preparation or conductive gel
was required. Sufficient pressure was applied to provide good electrical contact as evidenced by the best signal-to-noise ratio of the
detected signals. (In our system, this is accomplished by viewing the
detected signal on a computer screen in real time.) We detected
signals from various muscles from the face, neck, and in limbs. Here
we report on two muscles of the head and neck, the orbicularis oculi
and the platysma, and on one limb muscle, the tibialis anterior. The
head and neck muscles were chosen because they are not conveniently
or comfortably assayed with a needle sensor and because their
architecture differs from that of the more commonly studied limb
muscles that have unified origins and insertions. The tibialis anterior
was chosen as a typical representative of limb muscles. The sensor
array was placed in the following locations. In the tibialis anterior, it
was placed in the distal third (lengthwise) of the muscle; in the
orbicularis oculi, it was placed on the lateral aspect of the superior eye
lid; and in the platysma, it was placed on the lateral aspect of the neck,
halfway between mandible and clavicle.
In the tibialis anterior, we recorded the torque output of the muscle
and provided it as feedback to the subject so that a constant-force
isometric contraction was generated during the detection of the sEMG
signal to be decomposed. This was done by placing the limb in an
apparatus that restrained the movement of the limb and was instrumented with a high-stiffness force gauge (model MB-250; Interface,
Scottsdale, AZ). The torque output of the muscle generated during the
contraction was expressed as a percentage of the maximal torque
generated before the contraction. The subjects were asked to maintain
a constant force at 20 –50% MVC. In the muscles of the head and
neck, where it was not possible to record the torque, the feedback to
the subject was provided by the root-mean-squared (RMS) value of
the EMG signal detected with a standard sEMG differential sensor
(DE 2.1; Delsys, Boston, MA) placed adjacent to the array. This
differential sensor consists of a parallel-bar configuration having
detection surfaces that are 1 mm wide, 10 mm long, and spaced 10
mm apart. This sensor detects a global sEMG signal that is representative of the overall activity of the muscle being monitored. The global
sEMG signal was detected with a bandwidth of 20 – 450 Hz and the
RMS was calculated and smoothed using hardware circuitry. When
processed in this fashion, the time-varying RMS value provides a
reasonable analog to the force produced by the muscle (Basmajian and
De Luca 1985; Lawrence and De Luca 1983). The time-varying value
of the RMS of the global sEMG signal, or the estimated force, was
expressed as a percentage of the value obtained during a maximal
contraction. In the contraction of the orbicularis oculi, the subject was
requested to squint with one eye and to attempt to maintain the global
RMS constant. In the platysma, the subject was requested to tense the
neck musculature and to maintain the global RMS constant.
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1649
Challenges of sEMG decomposition
Any approach for decomposing EMG signals must be able to deal with
four major complexities that occur within the signal. These complexities
are shown in Fig. 3: 1) Superposition of action potentials from different
MUs, 2) Large dynamic range of the amplitudes among the action
potentials of different MUs of interest, 3) Shape changes across the
different action potentials of each MU (arising from slight movement
between the sensor and muscle fibers and/or intracellular processes), and
4) Similarity of shape at various times among the action potentials of
different MUs. These phenomena may also act in concert with each other
to make the decomposition task all the more difficult.
These complexities are accentuated in the sEMG signal. Sample
signals detected from the needle sensor and surface sensor array
(shown in Fig. 2A) are presented in Fig. 4. The signals in this figure
were obtained by inserting the needle sensor 1 cm beneath the surface
sensor array, which was placed in the middle (lengthwise) of the
tibialis anterior muscle. (Consequently, some of the action potentials
in each signal belong to the same MUs.) It is visually apparent that the
sEMG signal presents additional challenges for decomposition. First,
there is the issue of background clutter. The greater detection volume
of the array sensor leads to the sEMG signal containing action
potential contributions from a larger number of MUs and those with
smaller amplitudes will not be decomposable; they become part of the
noise component of the signal. Second, there is the issue of shape
similarity. The surface sensor array is located further away from the
action potential sources and thus the shapes and amplitudes of the
action potentials in the sEMG signal exhibit a smaller dynamic range;
in other words, they appear to be more similar in shape and amplitude.
Third, there is the augmented amount of superposition in the sEMG
signal. The intervening inhomogeneous tissue between the skin and
the action potential sources acts as a filter that, among other things,
increases the time duration of the action potentials and therefore the
amount of superposition.
Algorithms for decomposing sEMG signal
The PD III algorithms used to decompose the sEMG signal are
modified versions of those previously developed by us for the PD II
system (Hochstein et al., 2002; Nawab et al. 2002a,b, 2004a,b). The
algorithms in both PD II and PD III are designed to address the
difficulties inherent in trying to separate overlapping action potentials
at various points in the EMG signal by using an Artificial Intelligence
framework called IPUS (“Integrated Processing and Understanding of
Signals”) Lesser et al. 1995; Nawab and Lesser 1992; Winograd and
Nawab 1995). Although other signal-processing approaches have also
been investigated for performing EMG decompositions, the IPUS
approach has provided significantly more accurate results on intramuscular EMG data (Nawab et al. 2004a,b). The PD III system
represents our initial attempt at incorporating the IPUS approach into
the decomposition of surface EMG signals.
FIG. 3. Stylized examples of the various challenges presented by the
realistic behavior of EMG signals detected with indwelling sensors with small
detection volume and susceptible to movement.
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DE LUCA ET AL.
FIG. 4. Top: one channel of signal detected from the surface sensor array shown in Fig. 2A. Bottom: one channel of
signal detected simultaneously from a needle quadrifilar sensor. Both signals were detected during constant 30% maximal
voluntary contraction (MVC) isometric contractions from the
tibialis anterior muscle.
As illustrated in Fig. 5, the PD III system consists of four processing stages. It takes as input an sEMG signal, x(t), and produces as its
final output a set { y( j) (t) 兩 j ⫽ 1, 2, . . . , N} of N action potential
trains. PD III first applies a digital IIR band-pass filter (see the
APPENDIX for details) to the input EMG signal, x(t), and passes the
result, xB(t), through a maximum a posteriori probability (MAP)
receiver (LeFever and De Luca 1982) designed to avoid false positives in the classification of action potential detections (see the
APPENDIX for details). The MAP receiver may also be viewed as
( j)
producing estimates { yM
(t) 兩 j ⫽ 1, 2, . . . , NM} of NM action potential trains. The corresponding decomposition error is evaluated as the
NM
( j)
square of the error signal, eM(t) ⫽ xB(t) ⫺ ¥j⫽1
yM
(t). This error
arises primarily because in trying to avoid false positives the MAP
receiver 1) often splits off a motor unit train into two or more trains
because of shape differences among various action potentials of the
original train and 2) often fails to find a classification for signal data
that is composed of complex superpositions of several different action
potentials. Subsequent stages of processing are aimed at reducing
these two types of decomposition error.
The third PD III stage shown in Fig. 5 is designed to avoid
decomposition errors that arise because under certain conditions it is
possible for the MAP receiver to split the action potential train of a
motor unit into two or more separate trains. This stage uses a “trellis
traversal” search strategy (Castañon 1990) to identify trains that have
a high probability of having been split off from other trains and
merges them. The probability that a particular train may be “split off”
from another train is estimated on the basis of the degree to which the
template shapes of the two trains are correlated to each other and the
degree to which they are uncorrelated to the template shapes of any
other trains (see the APPENDIX for details).
The fourth PD III stage in Fig. 5 is designed to compensate for the
ineffectiveness of the MAP receiver in signal regions involving
complex superpositions. In such regions, significant interference between two or more action potentials makes the resultant data very
different from any other data encountered in the input signal. In such
cases, the MAP receiver declares the data as belonging to a new MU
train but never finds a matching action potential later in the signal.
Such “degenerate” trains are abandoned and their corresponding data
are reanalyzed by PD III’s fourth stage. The reanalysis is aimed at
identifying the nondegenerate trains from the MAP receiver that are
consistent with the data in the superposition regions. This reanalysis
begins with an iterative correlation strategy (see the APPENDIX for
details) to assign probabilities of occurrence to each nondegenerate
train in each superposition region. These iterative correlation results
then undergo a utility maximization process (Von Neumann and
Morgenstern 1944) to convert the probabilities of occurrence into
decisions about which MU trains are actually consistent with the
superposition data.
Interactive Editor
The results of the automatic decomposition could be improved by
using the Interactive Editor to identify and correct wrongly assigned
action potentials. Figure 6 shows an actual screen-shot of the pop-up
window that appears in response to a click on a gap in the bar plot on
the top, where each bar indicates a firing time of a motor unit. The
pop-up window and its associated functions allow the user to compare
the signal data in that gap with the various templates to determine
whether there is sufficient evidence to indicate that the program made
a mistake. In this particular instance, the operator judged that the
signal data matched the visual characteristics of the motor unit with
the gap in its bar plot. Consequently, the user was able to modify the
decomposition results as indicated by the same MU’s bar plot at the
bottom of Fig. 6. By using the interactive editor, it is possible to obtain
decomposition accuracies that approach 100%.
RESULTS
FIG. 5. Block diagram presenting the most important components of the
Precision Decomposition III (PD III) algorithms.
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Figure 7 shows a sample of the sEMG signal detected by the
four channels of the sensor array along with the bar plots
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FIG. 6. Interactive Editor program. Motor unit (MU) train at the top
contains an apparent error—the gap in the oval. By examining the underlying
3-channel EMG data it is possible to identify the unlabeled waveform as
belonging to MU 4. Interactive Editor facilitates the decision-making process
by providing local firing statistics for each motor unit, the ability to overlap a
motor unit template with the signal waveform, and graphical representation of
the residual signal energy after subtraction of the template.
identifying the location of the MU firings identified by the PD
III algorithms. The signal was detected from the tibialis anterior muscle while the subject was performing an isometric
contraction following a trapezoidal profile with a plateau of
50% MVC. The sEMG signal in each channel contains the
action potentials emanating from the same source in the proximity of the detection surfaces, although the shapes will be
different because of the location of each pair of detection
surfaces with respect to the source. This difference in the
action potential shapes is exploited by the decomposition
algorithms to identify and classify the individual action potentials of a specific MU. This characteristic is useful for the PD
III algorithms when action potentials superimpose and/or when
the shapes change as a result of movement of the sensor with
respect to the action potential source.
A dotted vertical line indicates the location of the identified
action potential in the sEMG signal. Note that it is occasionally
possible to visually identify individual action potentials, but for
the most part visual identification is not possible because of
superposition and considerable background noise. The sEMG
signals contain more action potentials than those that are
identified and tracked. The algorithms keep track of all the
signals (above a selected threshold) to track the firings of the
identified action potential trains. In this figure we show a short
epoch of the firings of six MU action potential trains that were
tracked continuously throughout the duration of the contraction. The PD III algorithms could sporadically identify the
action potentials of other MUs throughout the contraction. The
majority of the firings of the MUs with these sporadically
identifiable firings could not be resolved by the Interactive
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1651
Editor and, consequently, they were abandoned because they
provided little useful information. Figure 7 presents a segment
of the contraction where the force is increasing and two MUs
are recruited. The identification of new MUs is performed
automatically. Also in the 0.5-s epoch presented, there are
seven instances of superposition among the six concurrently
active MUs. In one instance near the proximity of the 6.95-s
mark, four MU action potentials superimposed and they were
correctly identified.
The remainder of the decomposition of the trapezoid contraction is presented in Fig. 8. This is the result of the edited
version, which required 12 h of expert user interaction. The
automatic decomposition was obtained in 8 min with an accuracy of 90.4%. Figure 8A presents all the firings of six MUs
throughout the complete duration of the 25-s-long contraction.
(We refer to this as the Bar Plot.) The dark line represents the
force produced by the subject. The arrows on the force axis
indicate the recruitment threshold of the MUs. Figure 8B
presents the interpulse interval, plotted vertically for each
firing of all the MUs. (We refer to this as the Dot Plot.) This
presentation is useful for visually identifying the errors in the
decomposition, which are seen as dots that fall either noticeably below or above the average firing interval. Figure 8C
presents the time-varying mean firing rates of each MU. The
time-varying mean firing rates are obtained by passing the
impulse trains corresponding to the firing trains of the top plot
through a unit-area Hanning filter of 1.0-s duration. The decreasing firing rate value as a function of time during the
constant force contraction is consistent with similar previous
observations by De Luca et al. (1996). The shapes of the action
potentials of the MUs for all four channels are presented in Fig.
8D. The shapes fluctuated throughout the contraction. The
thick line represents the mean value of the amplitude of the
action potentials and the thin, dashed lines above and below
represent the SD.
Several contractions from the platysma and the orbicularis
oculi were decomposed. In one case, we obtained an accuracy
of 91.3% from the automatic decomposition. Examples of
these decomposition results are shown in Fig. 9. In this figure
we present the edited firing times and time-varying mean firing
rates of the decomposed action potentials for identified MUs
from the orbicularis oculi and the platysma, along with the
estimated force by the RMS value of the global EMG signal.
These examples are typical of the current capability of rudimentary PD III.
Note that the contraction of the tibialis anterior (Fig. 8) was
relatively constant, whereas the contractions of the other two
muscles were more erratic (Fig. 9). The study participants
could not perform smooth sustained contractions with their
facial and neck muscles, such that the RMS of the EMG signal
fluctuated substantially. The fact that the signals could be
decomposed under this condition is a testament to the ability of
PD III.
Proof of accuracy and consistency
When one proposes to decompose a signal whose composition is not known a priori, it is incumbent on the proponent to
prove that the decomposition is performed accurately. In our
case it is also necessary to provide evidence that the rudimentary PD III decomposition technique provides information
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FIG. 7. Top: examples of the 4 channels of the sEMG signals detected by the surface array sensor from the tibialis anterior muscle during a 50% MVC
contraction. Dotted vertical lines indicate the location of identified motor unit action potentials. Bottom: firing times of 6 motor units extracted from the
decomposed sEMG signals above. Note that the location of the action potentials indicated by the bars corresponds to the identifiable pulses in each channel of
the sEMG signals. Algorithms were able to identify the recruitment of MU 5 and MU 6, events that are not visually recognizable in the sEMG signals. Also,
the algorithms resolved 7 cases of superpositions that are not obvious from the appearance of the sEMG signals.
similar to that of the more established PD II technique, which
decomposes the iEMG signal detected with indwelling sensors.
One obvious approach for proving accuracy would be to
construct mathematically a signal of known components (MU
action potential trains) and then proceed to decompose the
synthesized signal into the constituent action potentials and
check the veracity of the outcome. Over two decades ago
Mambrito and De Luca (1984) showed that if the shapes of the
action potentials remain invariant throughout the contraction,
this approach is not sufficiently challenging, even when white
noise having an amplitude 20% of the average amplitude of the
action potentials was added to the signal. The challenges
presented by real signals and identified in the preceding section
represent important complexities that must be dealt with.
Additionally, the algorithms must be able to cope with other
unforeseen signal components that are occasionally presented
by real data as a result of abrupt displacements of the sensor
with respect to the source of the action potentials. Consequently, we used the “two-source” technique that we first
introduced in 1984.
We placed a surface sensor array (Fig. 2A) on the skin above
the tibialis anterior muscle and a quadrifilar needle sensor was
inserted directly beneath the array sensor, approximately 1 cm
into the muscle. It was expected that some of the motor units
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would contribute signals to both sensors and others to only one
sensor, depending on the proximity of the muscle fibers to the
separate sensors. The EMG signal from each sensor was
automatically decomposed by the respective decomposition
algorithm and then independently further processed with the
assistance of the Interactive Editor. The firing times of the
motor units from both sensors are shown in Fig. 10. Note that
three of the five MUs decomposed from the iEMG signal were
also found in the sEMG. The expanded time sequence in the
middle indicates that the MUs are firing at precisely the same
time. This was so for 97.6%, or 996 sEMG-detected out of
1,021 iEMG-detected firings of the three MU action potential
trains. This proves that the rudimentary PD III can obtain the
same information from the sEMG signal as the PDII obtains
from the iEMG signal. The precise coincidence of all the action
potentials in three trains extracted from the two signals also
proves that both the PD II and the PD III decomposition
algorithms are able to accurately decompose the signals because the probability that both algorithms make exactly the
same errors in both signals is extremely small.
DISCUSSION
The first point is that it is possible to accurately decompose
sEMG signals detected noninvasively by surface sensors, even
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1653
FIG. 8. Results of a decomposition of the sEMG signal detected from the tibialis anterior muscle during a 50% MVC contraction. This automatic decomposition
yielded an accuracy of 90.4% of the presented data, which was edited with Interactive Editor. A: bar plot of the individual firings of 6 concurrently active motor units.
Continuous trace represents the isometric force generated by the subject. B: dot plot of the interpulse intervals of each motor unit. Vertical displacement indicates the
time between adjacent firings. C: plot of the time-varying mean firing rates of each motor unit. D: shapes of the action potentials of 6 motor units as recovered from
the raw sEMG signal through spike-triggered averaging. Mean (solid line) ⫾ SD (dashed line) are plotted for each action potential.
for unstable contractions ⱕ50% MVC. The algorithms were
able to automatically identify the recruitment and the derecruitment of MUs. This is an important feature of our system that
J Neurophysiol • VOL
is particularly relevant for physiological studies where the
presence of new MUs imparts physiological meaning. No other
reported attempt at decomposing the sEMG signal has made
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FIG. 9. Results of sEMG decomposition from the orbicularis oculi and platysma. Data are examples of motor unit firing patterns during a 30 and a 50% MVC
contraction from one subject. Plots on the left present the firing times of motor units; plots on the right represent the time-varying mean firing rates of the motor
units. Solid black lines represent the root-mean-squared value of the EMG signal detected with standard surface sensors, which provides an indication of the force
exerted by the muscle (see text for details).
such claims. However, the MU yield in a contraction was low;
it was typically less than five, compared with typically eight for
iEMG signals, as previously reported in Nawab et al. (2004
a,b). Although the algorithms of the PD III are still rudimentary
and the design of the sensor array was heuristic, we were able
to extract at least three MU action potential trains from approximately one half of the sEMG signal records that were
collected. We expect this yield to increase: 1) as the decomposition algorithms evolve to suit the specific characteristics of
the challenges presented by the sEMG signal and 2) as the
design of the sensor array evolves to provide channels that
render signals with greater distinction in the shapes of the
action potentials.
In addition to demonstrating a technical success, the decomposed data reveal two interesting physiological implications.
The first concerns the behavior of the firing rates. It is noteworthy, that during a contraction the hierarchical organization
of the firing rates of the orbicularis oculi and platysma, which
are innervated by the VII cranial nerve, appears similar to that
of the tibialis anterior, which is innervated by the L4, L5, and
S1 spinal nerves. In particular, values of the mean firing rates
of MUs are inversely proportional to their recruitment thresholds, such that earlier-recruited, low-threshold MUs maintain
higher firing rates than later-recruited, higher-threshold units.
(The occasional crossover between adjacent firing rates seen in
Figs. 8 and 9 can be attributed to noise components in the
FIG. 10. Comparison of decomposition results of simultaneously recorded indwelling EMG (iEMG) and sEMG signals. Open spaces on the sEMG
decomposition indicate some of the errors made by the algorithms. Note the perfect match for the motor unit firings in the expanded time interval.
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common drive to the motoneuron pool.) The layered appearance of the plots of the firing rates versus force of successively
recruited MUs led us to refer to this phenomenon as “onion
skin” (De Luca and Erim 1994; De Luca et al. 1982a). This
characteristic can be seen in data reported by other investigators who have signal processing knowledge (Freund et al.
1975; Hoffer et al. 1987; Masakado et al. 1995; McGill et al.
2005; Person and Kudina 1972; Rose and McGill 2001;
Stashuk and De Bruin 1988). Some (Gydikov and Kosarov
1974; Moritz et al. 2005; Taylor and Enoka 2004) have argued
against the hierarchical arrangement of the firing rates based on
simulation studies or by combining the firing rate versus time
curves of several subjects and/or contractions and emphasizing
the lack of the hierarchical arrangement. Clearly the latter
approach introduces intrasubject variations and cannot be used
to describe the relative behavior of firing rates present within a
contraction.
The second implication is that the dynamic range of the
firing rate of MUs innervated from the cranial nerves (i.e., the
orbicularis oculi and the platysma) is similar to that of the
small muscles of the hand, which are innervated by the cervical
nerves. De Luca et al. (1982a) reported that the firing rates of
the first dorsal interosseous reached values of 41 pps; Kukulka
and Clamann (1981) and Bigland-Ritchie et al. (1983) reported
those of the adductor pollicis to reach 32 and 35 pps, respectively. These firing rate values are in contrast to those observed
in large limb muscles, as exemplified by the tibialis anterior in
this study and various other muscles in previously reported
studies (Adam and De Luca 2005; De Luca and Erim 1994; De
Luca et al. 1982a,b). In the two examples provided in Fig. 9,
values of the firing rates of the orbicularis oculi and the
platysma have a considerably greater spread among MUs of
different recruitment threshold compared with the behavior of
the MUs in the tibialis anterior (Fig. 8). Also the firing rates
reach greater values in the orbicularis oculi and the platysma.
For example, during the 50% MVC contractions, the firing
rates of the platysma reach values of 35 pps, whereas those in
the tibialis anterior reach about 17 pps. Although speculative,
the higher dynamic range and absolute values of the firing rates
of the orbicularis oculi and the platysma might be explained by
a lack of recurrent inhibition by the Renshaw system. Its
absence has been reported by Windhorst (1996) in the nearby
oculomotor and masticatory muscles.
APPENDIX: SIGNAL PROCESSING DETAILS
Signal acquisition in PD III
●
●
●
●
Number of signal channels: 4
Sampling rate of input signal: 20,000 Hz
Analog antialiasing filter: fourth-order Butterworth filter; 24
dB/octave slope; 9,500 Hz cutoff
Analog high pass filter: second-order Butterworth filter; 12 dB/
octave slope; 100 Hz cutoff
First stage of PD III
●
●
●
Digital filter type: eighth-order Butterworth band-pass
Digital filter’s lower cutoff: 24 dB/octave ⬍250 Hz
Digital filter’s upper cutoff: 24 dB/octave ⬎2,000 Hz
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Second stage of PD III
INPUT TO MAP RECEIVER. The input to the MAP receiver stage is a
segmented version of xB(t), the filtered input signal. Each signal
segment is established by virtue of its signal amplitudes being, on
average, above a threshold that is determined adaptively by the
decomposition system in accordance with a set of dynamic range
criteria for the amplitudes of the decomposable MU trains. The
segmented signal is then used to initiate and update action potential
templates for each hypothesized MU. These templates are, in turn,
used by the MAP receiver to help classify signal components that are
detected by amplitude peaks in the segmented signal. The task of the
MAP receiver is to associate a hypothesized MU with each detected
signal component.
Denoting the ith MU by the symbol ui,
the MAP receiver assigns a particular MU, say, uk, to the signal
component such that
BASICS OF MAP RECEIVER.
P共uk兩␳兲 ⬎ P共ui兩␳兲
1 ⱕ i ⱕ M, i ⫽ k
where ␳ is the data vector for the detected pulse. In the presence of
white Gaussian noise with variance ␴2, the MAP decision criterion
may be stated as
兩 ␳ ⫺ s k兩2 ⫺ 2␴ 2 ln P共uk兲 ⬍ 兩␳ ⫺ si兩2 ⫺ 2␴ 2 ln P共ui兲
1 ⱕ i ⱕ M, i ⫽ k
where si is the data vector for the template of the ith MU, and P(ui) is
the a priori probability that the detected pulse belongs to the pulse
train ui. This a priori probability is estimated by a hazard function
calculation (LeFever and De Luca 1982), assuming that the first-order
interpulse intervals have a Gaussian density for each MU.
An EMG signal segment d is declared as an
initiation of a new motor unit train if for each preexisting template s,
the value ␣ that minimizes 兩 d ⫺ ␣s 兩2 is such that 兩 1 ⫺ ␣ 兩 ⬎ 0.5 or
兩 d ⫺ ␣s 兩2/兩 s 兩2 ⬎ 0.5.
TEMPLATE INITIATION.
When the MAP receiver determines a match
between segment data d and a preexisting template s(old), the segment
data are used to update the template in accordance with the recursive
relation: s(new) ⫽ 5⁄6s(old) ⫹ 1⁄6d. The initial template and all of its
subsequent updates are saved for later use by the decomposition
system.
TEMPLATE UPDATE.
Third stage of PD III
TEMPLATE SELECTION. When comparing two trains to determine
whether they are both split off from a single train, the decomposition
system selects the pair of templates (one from each train) that are
closest in time to each other.
PROBABILITY ESTIMATION. In comparing template sj with template
sk we estimate the probability that they are both from the same train
as Pj,k ⫽ ␤(sj 䡠 sk)/(兩 sj 兩兩 sk 兩), where the 䡠 symbol denotes the dotproduct operation; ␤ ⫽ ␣ for 0 ⱕ ␣ ⱕ 1, ␤ 1/␣ for ␣ ⬎ 1, ␤ ⫽ 0 for
␣ ⬍ 0, and ␣ ⫽ (sj 䡠 sk)/(sj 䡠 sj).
Fourth stage of PD III
The basic strategy is that a representative template for each MU
train is correlated against the entire filtered signal. The representative
template is selected as the one that was most recently updated. The
goal is to determine locations where the “maximal amplitude” MU,
i.e., the one whose representative template attains the greatest amplitude from among all the motor units, may be present. To achieve this
goal, the receiver first determines the locations where the correlation
function of the maximal MU has a local maximum that is greater than
the correlation values at that point of all the other MUs. At each of the
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determined locations, an estimate of the probability that the maximal
MU’s action potential actually occurred at the point is obtained as
P̂ ⫽ ␤共冑1 ⫺ 兩d ⫺ ␣p兩2/兩d兩2兲
where the template for the MU is represented by the vector p, the
corresponding signal data are represented by the vector d, ␣ is the
scale factor that minimizes the value of 兩 d ⫺ ␣p 兩2, and ␤ ⫽ ␣ if 0 ⱕ
␣ ⱕ 1; ␤ ⫽ 1/␣ if ␣ ⬎ 1; and ␤ ⫽ 0 if ␣ ⬍ 0.
Conceptually, ␣ represents the degree to which d and p are co-linear
and (d ⫺ ␣p) represents the orthogonal component of the modeling
error. A probability threshold is also established by the decomposition
system as the largest probability value obtained by correlating the
maximal-amplitude MU’s template with the templates of the remaining MUs. At all the locations where the estimated probability is above
the probability threshold, the template of the maximal MU (appropriately scaled) is subtracted from the sEMG signal to obtain the input
signal for the next iteration. The next iteration is the same as the
previous one except for the removal from consideration of the maximal MU of the previous iteration, thus leading to the declaration of
another MU as the maximal MU for that iteration. Once all the
iterations are complete, a utility maximization procedure is used for
converting probabilities of occurrence into decisions about which MU
may be actually responsible for which action potential contribution in
the signal data.
ACKNOWLEDGMENTS
The authors thank A. Morgan and A. Trongnetrpunya for assistance in
processing the data.
GRANTS
This work was supported by National Institutes of Health Bioengineering
Research Partnership Grant 1R24HD-38585 from the National Center for
Medical Rehabilitation Research of the National Institute of Child Health and
Human Development, the National Aeronautics and Space Administration
Grant NCC 9 –127 and Delsys Inc.
REFERENCES
Adam A and De Luca CJ. Recruitment order of motor units in human vastus
lateralis muscle is maintained during fatiguing contractions. J Neurophysiol
90: 2919 –2927, 2003.
Adam A and De Luca CJ. Firing rates of motor units in human vastus lateralis
muscle during fatiguing isometric contractions. J Appl Physiol 99: 268 –280,
2005.
Adam A, De Luca CJ, and Erim Z. Hand dominance and motor unit firing
behavior. J Neurophysiol 80: 1373–1382, 1998.
Andreassen S. Computerized analysis of motor unit firing. In: ComputerAided Electromyography, edited by Desmedt JE. Basel: Karger, 1983,
p. 150 –163.
Basmajian J and De Luca CJ. Muscles Alive (5th ed.). Baltimore, MD:
Williams & Wilkins, 1985.
Bigland-Ritchie B, Johansson R, Lippold OCJ, Smith S, and Woods JJ.
Changes in motoneuron firing rates during sustained maximal voluntary
contractions. J Physiol 340: 335–346, 1983.
Broman H. Knowledge-based signal processing in the decomposition of
myoelectric signals. IEEE Trans Eng Med Biol 7: 24 –28, 1988.
Castañon DA. Efficient algorithms for finding the K best paths through a
trellis. IEEE Trans Aerospace Electronic Syst 26: 405– 410, 1990.
Christodoulou CI and Pattichis CS. Unsupervised pattern recognition for the
classification of EMG signals. IEEE Trans Biomed Eng 46: 169 –178, 1999.
De Figueiredo RJP and Gerber A. Separation of superimposed signals by a
cross-correlation method. IEEE Trans Acoust Speech Signal Process 31:
1084 –1089, 1983.
De Luca CJ and Adam A. Decomposition and analysis of intramuscular
electromyographic signals. In: Modern Techniques in Neuroscience Research, edited by Windhorst U and Johansson H. Heidelberg, Germany:
Springer-Verlag, 1999, p. 757–776.
De Luca CJ and Erim Z. Common drive of motor units in regulation of
muscle force. Trends Neurosci 17: 299 –305, 1994.
J Neurophysiol • VOL
De Luca CJ, Foley PJ, and Erim Z. Motor unit control properties in
constant-force isometric contractions. J Neurophysiol 76: 1503–1516, 1996.
De Luca CJ and Forrest WJ. An electrode for recording single motor unit
activity during strong muscle contractions. IEEE Trans Biomed Eng 19:
367–372, 1972.
De Luca CJ, LeFever RS, McCue MP, and Xenakis AP. Behaviour of
human motor units in different muscles during linearly varying contractions.
J Physiol 329: 113–128, 1982a.
De Luca CJ, LeFever RS, McCue MP, and Xenakis AP. Control scheme
governing concurrently active human motor units during voluntary contractions. J Physiol 329: 129 –142, 1982b.
Erim Z, Beg MF, Burke D, and De Luca CJ. Effects of aging on motor-unit
control properties. J Neurophysiol 82: 2081–2091, 1999.
Fang J, Agarwal GC, and Shahani BT. Decomposition of multi-unit electromyographic signals. IEEE Trans Biomed Eng 46: 685– 697, 1999.
Finsen F, So
兾gaard K, Graven-Nielsen T, and Christensen H. Activity
patterns of wrist extensor muscles during wrist extensions and deviations.
Muscle Nerve 31: 242–251, 2005.
Freund HJ, Budingen HJ, and Dietz V. Activity of single motor units from
human forearm muscles during voluntary isometric contractions. J Neurophysiol 38: 933–946, 1975.
Garcia GA, Okuno R, and Akazawa K. Technological developments in
Japan—a decomposition algorithm for surface electrode-array electromyogram: a noninvasive, three-step approach to analyze surface EMG signals.
Eng Med Biol Mag IEEE 24: 63–72, 2005.
Gazzoni M, Farina D, and Merletti R. A new method for the extraction and
classification of single motor unit action potentials from surface EMG
signals. J Neurosci Methods 136: 165–177, 2004.
Gerstein GL and Clark WA. Simultaneous studies of firing patterns in
several neurons. Science 143: 1325–1327, 1964.
Glaser EM and Marks WB. The on-line separation of inter-leaved neuronal
pulse sequences. Rochester Conf. on Data Acquisition in Biology and
Medicine, 1966, p. 137–156.
Guiheneuc P, Doncarli C, and Le Bastard M. Concomitant multitrain
automatic analysis of motor unit potentials and macroEMG. In: ComputerAided Electromyography and Expert Systems, edited by Desmedt JE. Amsterdam: Elsevier, 1989, p. 83–90.
Gydikov A and Kosarov D. Some features of different motor units in human
biceps brachii. Pfluegers Arch 347: 75– 88, 1974.
Hochstein L, Nawab SH, and Wotiz R. An AI-based software architecture
for a biomedical application. SCI-2002. Proc 6th World Multiconf Systemics
Cybernetics, Informatics, Orlando, FL, July 2002, vol. XI, p. 60 – 64.
Hoffer JA, Sugano N, Loeb GE, Marks WB, O’Donovan MJ, and Pratt
CA. Cat hindlimb motoneurons during locomotion. II. Normal activity
patterns. J Neurophysiol 57: 530 –553, 1987.
Holobar A and Zazula D. Correlation-based decomposition of surface electromyograms at low contraction forces. Med Biol Eng Comput 42: 487– 495,
2004.
Iani C, Stalberg E, Falck B, and Bishoff C. New approaches to motor unit
potential analysis. Ital J Neurol Sci 15: 447– 459, 1994.
Jongen PJH, Vingerhoets HM, Roeleveld K, and Stegeman DF. Automatic
decomposition electromyography in idiopathic inflammatory myopathies.
J Neurol 243: 79 – 85, 1996.
Kamen G and De Luca CJ. Unusual motor unit firing behavior in older
adults. Brain Res 482: 136 –140, 1989.
Keehn DG. An interactive spike separation technique. IEEE Trans Biomed
Eng 13: 19 –28, 1966.
Kleine BU, Blok JH, Oostenveld R, Praamstra P, and Stegeman DF.
Magnetic stimulation-induced modulations of motor unit firings extracted
from multi-channel surface EMG. Muscle Nerve 23: 1005–1015, 2000.
Kukulka CG and Clamann HP. Comparison of the recruitment and discharge
properties of motor units in human brachial biceps and adductor pollicis
during isometric contractions. Brain Res 219: 45–55, 1981.
Lawrence JH and De Luca CJ. The myoelectric signal versus force relationship in different human muscles. J Appl Physiol 54: 1653–1659, 1983.
LeFever RS and De Luca CJ. Decomposition of superimposed action
potential trains. Proc 8th Annu Meeting Soc Neurosci, 299, November,
1978.
LeFever RS and De Luca CJ. A procedure for decomposing the myoelectric
signal into its constituent action potentials. Part I. Technique, theory and
implementation. IEEE Trans Biomed Eng 29: 149 –157, 1982.
Lesser V, Nawab SH, and Klassner F. IPUS: an architecture for the
integrated processing and understanding of signals. Artif Intell 77: 129 –171,
1995.
96 • SEPTEMBER 2006 •
www.jn.org
Innovative Methodology
DECOMPOSITION OF SURFACE EMG SIGNALS
Loudon GH, Jones NB, and Sehmi AS. New signal processing techniques for
the decomposition of EMG signals. Med Biol Eng Comput 30: 591–599,
1992.
Mambrito B and De Luca CJ. A technique for the detection, decomposition
and analysis of the EMG signal. Electroencephalogr Clin Neurophysiol 58:
175–188, 1984.
Masakado Y, Akaboshi K, Nagata M, Kimura A, and Chino N. Motor unit
firing behavior in slow and fast contractions of the first dorsal interosseous
muscle of healthy men. Electroencephalogr Clin Neurophysiol 97: 290 –
295, 1995.
Masuda T and De Luca CJ. Recruitment threshold and muscle fiber conduction velocity of single motor units. J Electromyogr Kinesiol 1: 116 –123,
1991.
Masuda T, Miyano H, and Sadoyama T. A surface sensor array for detecting
action potential trains of single motor units. Electroencephalogr Clin Neurophysiol 60: 453– 443, 1985.
McGill KC, Cummins KL, and Dorfman LJ. Automatic decomposition of
the clinical electromyogram. IEEE Trans Biomed Eng 32: 470 – 477, 1985.
McGill KC, Lateva ZC, and Marateb HR. EMGLAB: an interactive EMG
decomposition program. J Neurosci Methods 149: 121–133, 2005.
Moritz CT, Barry BK, Pascoe MA, and Enoka RM. Discharge rate variability influences the variation in force fluctuations across the working range
of a hand muscle. J Neurophysiol 93: 2449 –2459, 2005.
Nawab SH and Lesser V. Integrated processing and understanding of signals.
In: Symbolic and Knowledge-Based Signal Processing, edited by Oppenheim A and Nawab SH. Upper Saddle River, NJ: Prentice Hall, 1992, p.
251–285.
Nawab SH, Wotiz R, and De Luca CJ. Improved resolution of pulse
superpositions in a knowledge-based system for EMG decomposition. Proc
26th Int Conf IEEE Eng Med Biol Soc, San Francisco, CA, September
2004a, p. 69 –71.
Nawab SH, Wotiz R, and De Luca CJ. Resolving EMG pulse superpositions
via utility maximization. Proc 8th World Multiconf Systemics, Cybernetics,
Informatics, Orlando, FL, July 2004b, vol. XII, p. 233–236.
Nawab SH, Wotiz RP, Hochstein LM, and De Luca CJ. Improved decomposition of intramuscular EMG signals, SCI-2002. Proc 6th World Multiconfe Systemics, Cybernetics, Informatics, Orlando, FL, July 2002a, vol.
XIII, p. 274 –279.
Nawab SH, Wotiz RP, Hochstein LM, and De Luca CJ. Next-generation
decomposition of multi-channel EMG signals. Proc 2nd Joint Meeting IEEE
Eng Med Biol Soc and Biomed Eng Soc, Houston, TX, October 2002b, p.
36 –37.
J Neurophysiol • VOL
1657
Östlund N, Yu J, Roeleveld K, and Karlsson JS. Adaptive spatial filtering of
multichannel surface electromyogram signals. Med Biol Eng Comput 42:
825– 831, 2004.
Person RS and Kudina LP. Discharge frequency and discharge pattern of
human motor units during voluntary contraction of muscle. Electroencephalogr Clin Neurophysiol 32: 471– 483, 1972.
Roark RM, Schaefer SD, Li JCL, Adam A, and De Luca CJ. Multiple
motor unit recordings of laryngeal muscles. Laryngoscope 112: 2196 –2203,
2002.
Rose J and McGill KC. Muscle activation and motor unit-firing characteristics in cerebral palsy. Gait Posture 13: 285–286, 2001.
Simon W. The real-time sorting of neuro-electric action potentials in multiple
unit studies. Electroencephalogr Clin Neurophysiol 18: 192–195, 1965.
Sogaard K. Motor unit recruitment pattern during low-level static and dynamic contractions. Muscle Nerve 18: 292–300, 1995.
Stashuk D and de Bruin H. Automatic decomposition of selective needledetected myoelectric signals. IEEE Trans Biomed Eng 35: 1–10, 1988.
Tanji J and Kato M. Firing rate of individual motor units in voluntary
contraction of abductor digiti minimi muscle in man. Exp Neurol 40:
771–783, 1973.
Taylor AM and Enoka RM. Quantification of the factors that influence discharge
correlation in model motor neurons. J Neurophysiol 91: 796 – 814, 2004.
Türker KS, Miles TS, Smith N, and Nordstrom MA. On-line discrimination
of unit potentials on the basis of their waveforms: a new approach implemented on a personal computer. In: EMG of Jaw Reflexes in Man, edited by
van Steenberghe D and De Laat A. Leuven, Belgium: Leuven Univ. Press,
1989, p. 445– 451.
Von Neumann J and Morgenstern O. Theory of Games and Economic
Behavior. Princeton, NJ: Princeton Univ. Press, 1944.
Westad C and Westgaard RH. The influence of contraction amplitude and
firing history on spike-triggered averaged trapezius motor unit potentials.
J Physiol 562: 965–975, 2005.
Windhorst U. On the role of recurrent inhibitory feedback in motor control.
Prog Neurobiol 49: 517–587, 1996.
Winograd JM and Nawab SH. A C⫹⫹ software environment for the
development of embedded signal processing systems. ICASSP-95, Detroit,
MI, 1995, p. 2715–2719.
Zennaro D, Wellig P, Moschytz GS, Läubli T, and Krueger H. A decomposition software package for the decomposition of long-term multi-channel
electromyographic signals. Proc 23rd Annu Int Conf IEEE Eng Med Biol
Soc, 2001, p. 1070 –1073.
96 • SEPTEMBER 2006 •
www.jn.org